Thermal Behavior of Dodecylpyridinium-Based Surfactant Salts with Varied Anionic Constituent

Three novel dodecylpyridinium-based catanionic compounds were synthesized. A combination of techniques was used to examine their thermal properties: differential scanning calorimetry, powder x-ray diffraction, and polarizing microscopy. Briefly, the absence of mesomorphic properties and the formation of zig-zag blade textures were obtained in dodecylpyridinium picrate, thermotropic mesomorphism, and polymorphism in dodecylpyridinium dodecylbenzenesulfonate, and melting accompanied with degradation in combination with cholate. The anionic part of the molecule promotes behavior of novel compounds, resulting in different packing and thermal properties of catanionics in their solid state.      

Smart Determination of Difference Index for Asphaltene Stability Evaluation

Precipitation and deposition of asphaltene during different stages of petroleum production is recognized as problematic in oil industry because of the increase in production cost and the inhibition of a consistent flow of crude oil in different medium. Numerous correlations have been developed to determine asphaltene stability in crude oil. In this study, a novel ONN method was used to estimate difference index from SARA fraction data for rapid, accurate, and cost-effective determination of asphaltene stability. Neural networks are highly in danger of trapping in local minima. To eliminate this flaw, a hybrid genetic algorithm-pattern search technique was used instead of common back-propagation algorithm for training the employed neural network. A comparison between neural network and optimized neural network indicated superiority of optimized neural network.      

Prediction of Chemical Inhibitors Efficiency for Reducing Deposition Thickness Using Artificial Neural Network

Prediction of efficiency of chemical inhibitors to mitigation of deposition thickness is a key to developing crude oil transportation process. In this work, a feed-forward artificial neural network (ANN) algorithm has been applied to predict the influence of the mitigation effect of ethylene-co-vinyl acetate (EVA) copolymer and its combination with chloroform (C), acetone (A), P-xylene (PX), and petroleum ether (PE) on the deposition thickness in the pipeline. An optimized three-layer feed-forward ANN model using properties of the oil pipeline such as: inlet oil temperature, environmental (coolant mixture) temperature, oil Reynolds numbers; properties of injected inhibitor such as molecular weight, boiling point, and amount of injection; and time is presented. Different networks are considered and trained using 62661 data sets; the accuracy of the network is validated by 20888 testing data sets. To verify the network generalization, 29 different experiment data sets of four different set of inhibitors have been considered. It is found that the proposed ANN model is an alternative to experimentation and predicts deposition thickness without experimentation, vast information, and tedious and time-consuming calculations.     

Characterization of Colloidal Gas Aphron-Fluids Produced from a New Plant-Based Surfactant

Colloidal gas aphrons (CGAs) are gas bubbles with diameters ranging from 10 to 100 microns, generated by intense stirring of a surfactant solution at high speed. The surface activity and aggregation behavior of the surfactant affects the size/size distribution, stability, and other physicochemical properties of generated aphrons. Therefore, selection of a suitable surfactant is important for the generation of microbubbles with the desired properties. The goal of this articleis to investigate the potential use of a new plant-derived surfactant as an aphronizer surfactant in preparation of CGA-based drilling fluids for accomplishing desirable rheological and filtration properties. For this purpose, natural surfactant obtained from leaves of special tree, namely, Zizyphusspina Christi and used for preparation of aphron-based fluids. To achieve the research objectives, laboratory tests of suspension generation, microscopic visualization, initial yield, filtration loss, and rheological characterization with varying concentrations of surfactant and polymer were performed. Experimental results demonstrate that newly proposed biosurfactant has a great potential for application in preparation of CGA-based drilling fluids for implementation in petroleum drilling industry.